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A hybrid meta on-top functional for multiconfiguration pair-density functional theory.

Jie J Bao1,2, Dayou Zhang1,2, Shaoting Zhang1,3

  • 1Department of Chemistry, Chemical Theory Center, University of Minnesota, Minneapolis, MN 55455-0431.

Proceedings of the National Academy of Sciences of the United States of America
|January 10, 2025
PubMed
Summary

A new MC23 functional enhances multiconfiguration pair-density functional theory (MC-PDFT) by incorporating kinetic energy density. This hybrid meta functional shows improved performance for both strongly and weakly correlated systems.

Keywords:
MC-PDFTdatabasedensity functional theorymeta-GGAstrong correlation

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Area of Science:

  • Quantum Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Multiconfiguration pair-density functional theory (MC-PDFT) is an emerging method for electronic structure calculations.
  • Current MC-PDFT functionals are primarily based on generalized gradient approximations (GGAs) and lack advanced features.
  • Meta-generalized gradient approximation (meta-GGA) functionals have demonstrated superior accuracy in Kohn-Sham density functional theory (KS-DFT).

Purpose of the Study:

  • To develop and optimize a novel hybrid meta-GGA functional for MC-PDFT.
  • To introduce kinetic energy density into MC-PDFT functionals for improved accuracy.
  • To evaluate the performance of the new functional across diverse chemical systems.

Main Methods:

  • Development of a hybrid on-top functional incorporating kinetic energy density for MC-PDFT.
  • Optimization of functional parameters using a custom-built training database.
  • Benchmarking against existing KS-DFT functionals for strongly and weakly correlated systems.

Main Results:

  • Introduction of the MC23 functional, a hybrid meta-GGA for MC-PDFT.
  • MC23 demonstrates enhanced accuracy compared to standard KS-DFT functionals.
  • Improved performance observed for both strongly and weakly correlated electronic systems.

Conclusions:

  • The MC23 functional represents a significant advancement in MC-PDFT.
  • Incorporating kinetic energy density improves functional performance in MC-PDFT.
  • MC23 is recommended for future applications in computational chemistry and materials science.